
'''simple docstring'''
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.schedulers.scheduling_utils import SchedulerMixin
from diffusers.utils import BaseOutput, deprecate


@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->DDIM
class 			SCREAMING_SNAKE_CASE__      (     UpperCAmelCase__):
				lowerCAmelCase_     			=							42
				lowerCAmelCase_     			=							None







def 		A_(     A						:		str		,     A						:		str=0.999		,     A						:		Optional[Any]="cosine"		,     ):
							if alpha_transform_type == "cosine":

														def alpha_bar_fn(A						:		int):
																					return math.cos((t + 0.008) / 1.008 * math.pi / 2) ** 2

							elif alpha_transform_type == "exp":

														def alpha_bar_fn(A						:		List[str]):
																					return math.exp(t * -12.0)

							else:
														raise ValueError(f'''Unsupported alpha_tranform_type: {alpha_transform_type}''')

							UpperCamelCase   			=     []
							for i in range(UpperCamelCase__):
														UpperCamelCase   			=     i / num_diffusion_timesteps
														UpperCamelCase   			=     (i + 1) / num_diffusion_timesteps
														betas.append(min(1 - alpha_bar_fn(UpperCamelCase__) / alpha_bar_fn(UpperCamelCase__)		,     UpperCamelCase__))
							return torch.tensor(UpperCamelCase__		,     dtype=torch.floataa)





class 			SCREAMING_SNAKE_CASE__      (     UpperCAmelCase__							,       UpperCAmelCase__):
				lowerCAmelCase_     			=							1







				@register_to_config
				def __init__(       self						,				A_ = 1000						,				A_ = 0.0_001						,				A_ = 0.02						,				A_ = "linear"						,				A_ = None						,				A_ = True						,				A_ = True						,				A_ = 0						,				A_ = "epsilon"						,				A_ = 1.0						,				**A_						,				)->		str:







											'''simple docstring'''






											if kwargs.get('set_alpha_to_one'						,				A_     ) is not None:
																		UpperCamelCase   			=     (
																		    'The `set_alpha_to_one` argument is deprecated. Please use `set_alpha_to_zero` instead.'
																		)
																		deprecate('set_alpha_to_one'						,				'1.0.0'						,				A_						,				standard_warn=A_     )
																		UpperCamelCase   			=     kwargs['set_alpha_to_one']
											if trained_betas is not None:
																		UpperCamelCase   			=     torch.tensor(A_						,				dtype=torch.floataa     )
											elif beta_schedule == "linear":
																		UpperCamelCase   			=     torch.linspace(A_						,				A_						,				A_						,				dtype=torch.floataa     )
											elif beta_schedule == "scaled_linear":
																		# this schedule is very specific to the latent diffusion model.
																		UpperCamelCase   			=     (
																		    torch.linspace(beta_start**0.5						,				beta_end**0.5						,				A_						,				dtype=torch.floataa     ) ** 2
																		)
											elif beta_schedule == "squaredcos_cap_v2":
																		# Glide cosine schedule
																		UpperCamelCase   			=     betas_for_alpha_bar(A_     )
											else:
																		raise NotImplementedError(F'''{beta_schedule} does is not implemented for {self.__class__}'''     )

											UpperCamelCase   			=     1.0 - self.betas
											UpperCamelCase   			=     torch.cumprod(self.alphas						,				dim=0     )

											# At every step in inverted ddim, we are looking into the next alphas_cumprod
											# For the final step, there is no next alphas_cumprod, and the index is out of bounds
											# `set_alpha_to_zero` decides whether we set this parameter simply to zero
											# in this case, self.step() just output the predicted noise
											# or whether we use the final alpha of the "non-previous" one.
											UpperCamelCase   			=     torch.tensor(0.0     ) if set_alpha_to_zero else self.alphas_cumprod[-1]

											# standard deviation of the initial noise distribution
											UpperCamelCase   			=     1.0

											# setable values
											UpperCamelCase   			=     None
											UpperCamelCase   			=     torch.from_numpy(np.arange(0						,				A_     ).copy().astype(np.intaa     )     )







				def 						UpperCAmelCase_    (       self						,				A_						,				A_ = None     )->		Union[str, Any]:







											'''simple docstring'''






											return sample







				def 						UpperCAmelCase_    (       self						,				A_						,				A_ = None     )->		str:







											'''simple docstring'''






											if num_inference_steps > self.config.num_train_timesteps:
																		raise ValueError(
																		    F'''`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:'''
																		    F''' {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle'''
																		    F''' maximal {self.config.num_train_timesteps} timesteps.'''     )

											UpperCamelCase   			=     num_inference_steps
											UpperCamelCase   			=     self.config.num_train_timesteps // self.num_inference_steps
											# creates integer timesteps by multiplying by ratio
											# casting to int to avoid issues when num_inference_step is power of 3
											UpperCamelCase   			=     (np.arange(0						,				A_     ) * step_ratio).round().copy().astype(np.intaa     )
											UpperCamelCase   			=     torch.from_numpy(A_     ).to(A_     )
											self.timesteps += self.config.steps_offset







				def 						UpperCAmelCase_    (       self						,				A_						,				A_						,				A_						,				A_ = 0.0						,				A_ = False						,				A_ = None						,				A_ = True						,				)->		Tuple:







											'''simple docstring'''






											UpperCamelCase   			=     timestep + self.config.num_train_timesteps // self.num_inference_steps

											# 2. compute alphas, betas
											# change original implementation to exactly match noise levels for analogous forward process
											UpperCamelCase   			=     self.alphas_cumprod[timestep]
											UpperCamelCase   			=     (
											    self.alphas_cumprod[prev_timestep]
											    if prev_timestep < self.config.num_train_timesteps
											    else self.final_alpha_cumprod
											)

											UpperCamelCase   			=     1 - alpha_prod_t

											# 3. compute predicted original sample from predicted noise also called
											# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
											if self.config.prediction_type == "epsilon":
																		UpperCamelCase   			=     (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
																		UpperCamelCase   			=     model_output
											elif self.config.prediction_type == "sample":
																		UpperCamelCase   			=     model_output
																		UpperCamelCase   			=     (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
											elif self.config.prediction_type == "v_prediction":
																		UpperCamelCase   			=     (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
																		UpperCamelCase   			=     (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
											else:
																		raise ValueError(
																		    F'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or'''
																		    ' `v_prediction`'     )

											# 4. Clip or threshold "predicted x_0"
											if self.config.clip_sample:
																		UpperCamelCase   			=     pred_original_sample.clamp(
																		    -self.config.clip_sample_range						,				self.config.clip_sample_range     )

											# 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
											UpperCamelCase   			=     (1 - alpha_prod_t_prev) ** 0.5 * pred_epsilon

											# 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
											UpperCamelCase   			=     alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

											if not return_dict:
																		return (prev_sample, pred_original_sample)
											return DDIMSchedulerOutput(prev_sample=A_						,				pred_original_sample=A_     )







				def __len__(       self     )->		Optional[int]:







											'''simple docstring'''






											return self.config.num_train_timesteps





'''simple docstring'''




import re

from flax.core.frozen_dict import freeze
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.experimental import PartitionSpec as P


# Sentinels
__UpperCAmelCase					=object()

# For specifying empty leaf dict `{}`
__UpperCAmelCase					=object()






def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__							)      ->       List[Any]:
    __lowerCamelCase            =    tuple((re.compile(x + '''$'''							) for x in qs)							)
    for i in range(len(UpperCamelCase__							) - len(UpperCamelCase__							) + 1							):
        __lowerCamelCase            =    [x.match(UpperCamelCase__							) for x, y in zip(UpperCamelCase__							,    ks[i:]							)]
        if matches and all(UpperCamelCase__							):
            return True
    return False






def __lowerCAmelCase					(				UpperCamelCase__							)      ->       Optional[int]:
    def replace(UpperCamelCase__							,    UpperCamelCase__							):
        for rule, replacement in rules:
            if _match(UpperCamelCase__							,    UpperCamelCase__							):
                return replacement
        return val

    return replace






def __lowerCAmelCase					(				)      ->       Dict:
    return [
        # embeddings
        (("transformer", "wpe", "embedding"), P('''mp'''							,    UpperCamelCase__							)),
        (("transformer", "wte", "embedding"), P('''mp'''							,    UpperCamelCase__							)),
        # atention
        (("attention", "(q_proj|k_proj|v_proj)", "kernel"), P(UpperCamelCase__							,    '''mp'''							)),
        (("attention", "out_proj", "kernel"), P('''mp'''							,    UpperCamelCase__							)),
        (("attention", "out_proj", "bias"), None),
        # mlp
        (("mlp", "c_fc", "kernel"), P(UpperCamelCase__							,    '''mp'''							)),
        (("mlp", "c_fc", "bias"), P('''mp'''							)),
        (("mlp", "c_proj", "kernel"), P('''mp'''							,    UpperCamelCase__							)),
        (("mlp", "c_proj", "bias"), None),
        # layer norms
        ((r"ln_\d+", "bias"), None),
        ((r"\d+", r"ln_\d+", "scale"), None),
        (("ln_f", "bias"), None),
        (("ln_f", "scale"), None),
    ]






def __lowerCAmelCase					(				UpperCamelCase__							)      ->       int:
    __lowerCamelCase            =    _get_partition_rules()
    __lowerCamelCase            =    _replacement_rules(UpperCamelCase__							)
    __lowerCamelCase            =    {k: _unmatched for k in flatten_dict(UpperCamelCase__							)}
    __lowerCamelCase            =    {k: replace(UpperCamelCase__							,    UpperCamelCase__							) for k, v in initd.items()}
    assert _unmatched not in result.values(), "Incomplete partition spec."
    return freeze(unflatten_dict(UpperCamelCase__							)							)
